return matrix(0, (self.dimension(), 1), tc='d')
- def _A(self):
+ def A(self):
"""
Return the matrix ``A`` used in our CVXOPT construction.
>>> e1 = [1,1,1]
>>> e2 = [1,2,3]
>>> SLG = SymmetricLinearGame(L, K, e1, e2)
- >>> print(SLG._A())
+ >>> print(SLG.A())
[0.0000000 1.0000000 2.0000000 3.0000000]
<BLANKLINE>
return matrix([-1, self._zero()])
- def _C(self):
+ def C(self):
"""
Return the cone ``C`` used in our CVXOPT construction.
>>> e1 = [1,2,3]
>>> e2 = [1,1,1]
>>> SLG = SymmetricLinearGame(L, K, e1, e2)
- >>> print(SLG._C())
+ >>> print(SLG.C())
Cartesian product of dimension 6 with 2 factors:
* Nonnegative orthant in the real 3-space
* Nonnegative orthant in the real 3-space
@staticmethod
- def _b():
+ def b():
"""
Return the ``b`` vector used in our CVXOPT construction.
>>> e1 = [1,2,3]
>>> e2 = [1,1,1]
>>> SLG = SymmetricLinearGame(L, K, e1, e2)
- >>> print(SLG._b())
+ >>> print(SLG.b())
[1.0000000]
<BLANKLINE>
soln_dict = solvers.conelp(self._c(),
self._G(),
self._h(),
- self._C().cvxopt_dims(),
- self._A(),
- self._b(),
+ self.C().cvxopt_dims(),
+ self.A(),
+ self.b(),
options=opts)
except ValueError as error:
if str(error) == 'math domain error':
True
"""
- return (condition_number(self._G()) + condition_number(self._A()))/2
+ return (condition_number(self._G()) + condition_number(self.A()))/2
def dual(self):